Abstract
GPU acceleration is useful in solving complex chemical information problems. Identifying unknown structures from the mass spectra of natural product mixtures has been a desirable yet unresolved issue in metabolomics. However, this elucidation process has been hampered by complex experimental data and the inability of instruments to completely separate different compounds. Fortunately, with current high-resolution mass spectrometry, one feasible strategy is to define this problem as extending a scaffold database with sidechains of different probabilities to match the high-resolution mass obtained from a high-resolution mass spectrum. By introducing a dynamic programming (DP) algorithm, it is possible to solve this NP-complete problem in pseudo-polynomial time. However, the running time of the DP algorithm grows by orders of magnitude as the number of mass decimal digits increases, thus limiting the boost in structural prediction capabilities. By harnessing the heavily parallel architecture of modern GPUs, we designed a “compute unified device architecture” (CUDA)-based GPU-accelerated mixture elucidator (G.A.M.E.) that considerably improves the performance of the DP, allowing up to five decimal digits for input mass data. As exemplified by four testing datasets with verified constitutions from natural products, G.A.M.E. allows for efficient and automatic structural elucidation of unknown mixtures for practical procedures.Graphical abstract.
Highlights
Mass spectrometry (MS) is one of the most widely used analytical methods for identifying the components of unknown mixtures or natural products
We present a graphical processing units (GPUs)-accelerated algorithm, the GPU-accelerated mixture elucidator (G.A.M.E.), that can efficiently promote performance improvements of dynamic programming (DP) algorithms used to resolve chemical structures in the mass spectra of an unknown mixture
Our G.A.M.E. method is executed in silico to predict the constituents of a mixture on the basis of its mass spectral data by exploiting a scaffold database with sidechain probability information, such as Harn’s Natural Product Scaffold database
Summary
Mass spectrometry (MS) is one of the most widely used analytical methods for identifying the components of unknown mixtures or natural products. Elucidation of chemical structures, especially from natural products, is important to identify potential drug candidates with fewer adverse effects and structural novelty for drug discovery [1]. Mass spectra indicate the mass-tocharge ratio of each component, and the amplitude of each peak roughly represents the relative abundance of the molecule. Additional techniques such as nuclear magnetic resonance (NMR) [2], a time-consuming and complex procedure, are needed to fully identify each component in the mixture.
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